The key elements that underpin food security require the adaptation of agricultural systems to support productivity increases while minimizing inputs and the adverse effects of climate change. The advances in precision agriculture over the past few years have substantially enhanced the efficiency of applying spatially variable agronomic inputs for irrigation, such as fertilizers, pesticides, seeds, and water, and we can attribute them to the increasing number of innovations that utilize new technologies that are capable of monitoring field crops for varying spatial and temporal changes. Remote sensing technology is the primary driver of success in precision agriculture, along with other technologies, such as the Internet of Things (IoT), robotic systems, weather forecasting technology, and global positioning systems (GPSs). More specifically, multispectral imaging (MSI) and hyperspectral imaging (HSI) have made the monitoring of the field crop health to aid decision making and the application of spatially and temporally variable agronomic inputs possible. Furthermore, the fusion of remotely sensed multisource data—for instance, HSI and LiDAR (light detection and ranging) data fusion—has even made it possible to monitor the changes in different parts of an individual plant. To the best of our knowledge, in most reviews on this topic, the authors focus on specific methods and/or technologies, with few or no comprehensive reviews that expose researchers, and especially students, to the vast possible range of remote sensing technologies used in agriculture. In this article, we describe/evaluate the remote sensing (RS) technologies for field crop monitoring using spectral imaging, and we provide a thorough and discipline-specific starting point for researchers of different levels by supplying sufficient details and references. We also high light strengths and drawbacks of each technology, which will help readers select the most appropriate method for their intended uses.

Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.